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«A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Approved November 2014 by the Graduate ...»

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alternate employment options in the context of Chitwan, so it is very likely that one is the alternative choice over the other. Further, an individual’s migration could be used for other household members to start a new business in Chitwan. This will be examined in the next analysis: the transition to the first business outside the home.

Interestingly, household capitals show no effects on the transition to the first salary employment of an individual when individual characteristics are controlled. Most of all, it seems like education plays an important role in the decision making of getting a salary job. In Model 3, one additional year in the education of an individual increases the likelihood of the transition to the first salary employment by 11%. When his or her father went to a school before, an individual is 64% more likely to get a salary job than an individual who does not have an educated father. The education of a mother does not affect the transition though. This is probably due to the patriarchal nature of Nepal society as observed in the result of gender: a female is 90% less likely to have a salary job than a male.

The interaction effects are summarized in Table 12B. Model 4 tests the interactions between migration and human capital, Model 5 tests the interactions between migration and natural capital, Model 6 tests the interactions between migration and physical capital, Model 7 tests the interactions between migration and financial capital, and Model 8 tests the interactions between migration and social capital. The interaction results show significant interaction effect between domestic migration and social capital only, and this result is visualized in Figure 20. As expected, when there are many individuals working in the non-farm sector in the same neighborhood, additional years of

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other hand, when there are not many individuals working in the non-farm sector in the same neighborhood, additional years of migration is more likely to keep an individual in what they have been doing.

In sum, the analysis results support the hypotheses HB1 and HB6-9. In detail, the result of the level of education supports the hypothesis HB1 stating that households rich in human capital are associated with a higher chance of the changes in the modes of production. And the result of the interaction between migration and the proportion of individuals having a salary job in the same neighborhood supports the hypothesis HB6-9 stating that migration has a positive association with the changes in the modes of production in case there are many households in the same neighborhood with a person having a salary employment.

Transition to the First Business outside the Home. The third analysis is to test the effects of migration, individual characteristics and household capitals on the transition to the first business outside the home. This is an individual-level analysis. Due to the limitations in data structure, yearly migration information from the Life History Calendar, not monthly migration information from the Household Registry, is tested. As a result, domestic and international migrations are used as the main variables to test migration effects.

As discussed, the most appropriate model for this analysis is the multilevel binary logistic model using discrete-time event history approach. In this model, the transition to the first business outside the home is predicted by migration experience at the individual

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transition. Since this analysis is using the Life History Calendar exclusively, yearly domestic and international migrations will be tested in each model. And based on the dataset I used for this analysis, which includes all the individuals from the households who took the survey in 1996, 2001, and 2006, a total 89 individuals out of 1,804 individuals who had not had any experience in business outside the home before 1996 experienced the transition between 1996 and 2008.

The main effects of migration and household capitals are summarized in Table 13A. Model 1 includes domestic and international migration of a respondent and other household members, and Model 2 includes individual characteristics in addition to migration, and household capitals are added in Model 3. The results are presented as odds ratios, so a coefficient greater than one represents a positive effect that accelerates the rate of the transition, while a coefficient less than one represents a negative effect that delays the transition.

Controlling for individual characteristics and household capitals, the result of Model 3 shows that the duration of domestic migration of other household members affects the transition to the first business outside the home. One additional year in the duration of domestic migration is associated with 22% increase in the likelihood of the transition. This result complements the result of migration in the analysis of the transition to the first salary employment. While an individual’s migration experience goes against an individual having a salary job, the same migration supports other household members to start a new business outside the home.

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positive effect on the transition, as does the father’s previous outside the home job experience before the individual was twelve years old. In detail, one additional year in education is associated with a 10% increase in the likelihood of the transition to the first business outside the home. When a father had previous work experience outside the home, the likelihood of the transition is 33% less than an individual whose father does not have work experience outside the home. This result is the opposite of the expectation, and my speculation is that this is due to the father’s experience and knowledge in unstable conditions of the market in the non-farm sector in Nepal.





Household capitals also have some impacts on the transition. In terms of natural capital, a one unit increase in the perception of current water quality compared to three years ago (better water quality) is associated with a 48% increase in the likelihood of the transition to the first business outside the home. This result is the opposite of the expectation. When it comes to physical capital, one additional consumer item is associated with a 33% increase in the likelihood of the transition, and a one unit increase in household quality (better housing) is associated with an 11% increase. The number of poultry is also positively associated with the transition: one additional poultry increases the likelihood of the transition to the first business outside the home by.05%.

The interaction effects are summarized in Table 13B. Model 4 tests the interactions between migration and human capital, Model 5 tests the interactions between migration and natural capital, Model 6 tests the interactions between migration and physical capital, Model 7 tests the interactions between migration and financial capital, and Model 8 tests the interactions between migration and social capital. The interaction

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capital only, and this result is visualized in Figure 21. Having many household members of working age represses the migration effect. For example, when a household with no member of working age is compared with a household with four members in the same age group, the latter household is more likely than the former household to start an out of home business as the duration of migration increases. Again, this result supports the previous argument that having additional labor in a household with the support of migration gives more options for rural households to diversify their income sources.

In sum, the analysis results support the hypotheses HB1, HB3a, and HB4. In detail, the result of the level of education support the hypothesis HB1 stating that households rich in human capital are associated with a higher chance of the changes in the modes of production. The result of consumer items supports the hypothesis HB3a stating that households rich in physical capital are associated with a higher chance of the changes in the modes of production. Last, the result of poultry supports the hypothesis HB4 stating that households rich in financial capital are associated with a higher chance of the changes in the modes of production.

Considering all the results across the models for the changes in the modes of production, the analysis results show significant associations between migration and the modes of production. It seems that migrating and having a job in the non-farm sector is exclusive to each other and it is an important livelihood choice over the other. In addition, although household capitals do not have strong influence on the changes in the modes of production when individual characteristics are controlled, the results indicate that changing the modes of production is an important decision making process at the

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Note: † p.10; * p.05; ** p.01, two tailed.

Table 12A Transition to the First Salary Employment at the Individual Level, Logistic Model Results

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Note: † p.10; * p.05; ** p.01, two tailed.

Figure 19 Interaction between Migration and Housing Quality, Transition out of Farming Note: Only coefficients of migration, housing quality, and the interaction between those two are used for the computation.

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Note: Only coefficients of migration, proportion of individuals having a salary job in the same neighborhood, and the interaction between those two are used for the computation.

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Note: Only coefficients of migration, number of household members of working age, and the interaction between those two are used for the computation.

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The analysis in this section is to test the effects of migration and household capitals on the transition from traditional energy sources, such as fuel wood, saw dust, and biomass, to modern energy sources, such as gas, kerosene, and electricity. This is a household-level analysis. As discussed, the most appropriate model for this analysis is the multilevel binary logistic model using a discrete-time event history approach. In this model, the transition from traditional energy sources to modern ones is the result of migration experience and household capitals a household had before the transition. All the households regardless of any status are included in the sample since every single household uses at least one type of energy no matter what.

First, we look at the number of households that used each energy source in three survey years summarized in Table 14. It shows that most households used fuel wood as their primary energy source for cooking in all three years despite the fact that modern energy sources, such as electricity, became widely available in most neighborhoods in Chitwan since 1996. At the neighborhood level, the data shows that electricity was available in about a half of 171 neighborhoods in 1996 and the number increased to about ninety eight percent of all the neighborhoods in 2001 and 2003. As a matter of fact, the number of households that used electricity as one of their primary energy source substantially increased between 2001 and 2006, but the number was still not as large as the one for fuel wood. In 1996, there were only 13 households using electricity, but the number became 206 households in 2006. Among the modern energy sources, gas became popular over time and even more popular than electricity in 2006 while kerosene significantly lost the popularity between 1996 and 2006; only 28 households used

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sources, besides fuel wood, the trend of biogas is noticeable. In 1996, there were only 59 households using it, but the number increased to 223 in 2006.

These numbers, however, do not show how many households actually moved from the use of one energy source to another. To explore more about the actual transition in detail, we first look at the number of households for each transition. Adapting the perspective of the energy stack, I consider multiple energy sources, which is the mixture of traditional and modern energy sources, as one of the categories of the transition. As a result, there are three bi-directional energy transitions we can observe; between only traditional energy sources and only modern energy sources, between only traditional energy sources and multiple energy sources, and between only modern energy sources and multiple ones. Figure 22 shows the patterns of these transitions in Chitwan, Nepal, between 1996 and 2001, and Figure 23 shows the patterns between 2001 and 2006.

Throughout the text, the term “traditional and modern energy sources” will be referred to as “multiple energy sources” to simplify the term.

Within both time periods, many households in Chitwan stayed using only traditional energy sources for everyday use, mostly fuel wood as indicated in Table 14.

And not many households transitioned from traditional energy sources to modern ones in spite of the availability of electricity in Chitwan as discussed before. This might indicate that availability does not fully account for the use of modern energy sources. In addition, a substantial number of households moved from only traditional energy sources to multiple energy sources during both time periods: 94 households in the first period and 116 households in the second period. And a small number of households moved from

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31 households in the second time period. These two patterns of energy transition implies that there might be a hierarchy in the use of energy sources as only traditional ones in the lowest place, multiple ones above that, and only modern ones on top of the hierarchy. If this is the case, then it might mean that the perspective of the energy stack and ladder are all valid but just need to be combined together.

However, considering the hierarchy, there are a noticeable number of households that move back to the previous stage in both time periods: from multiple energy sources to only traditional ones and from modern ones to only traditional or multiple ones. This pattern is clear especially between multiple energy sources and only traditional ones.



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